Addressing the requirements of SFTR and how it will impact the industry. The course will teach the strategies to prevent undesirable impacts and how to utilise the benefits of the new requirements, sâ¦

Following success in New York, we are bringing our cyber risk course to London for the first time to provide delegates with best practice strategies for ownership of cyber risk management and businesâ¦

Energy Risk Asia Awards 2018 submission is now open. Submission period ends on 27 September 2018. The Energy Risk Asia Awards recognise excellence across Asian commodities market as well as providingâ¦

Being recognised at the Hedge Funds Review European Performance Awards 2018 is the high point of any single manager or fund of hedge fund operating in Europe. The awards are recognised as the most prâ¦

This yearâs Markets Technology Awards takes place in London on November 27th. As part of the Risk Awards, it brings together a complete cross-section of the market: alongside technology vendors arâ¦

This white paper discusses the key challenges and opportunities facing banks as they prepare to implement the Fundamental Review of the Trading Book standard. It further examines how data aggregationâ¦

This white paper examines the key elements of Basilâs updated rules for IRRBB and the effect they will have on a banksâ ALM strategy. It further explores how a well-thought-out tenor mismatch strategâ¦

Abstract

This paper develops efficient importance sampling schemes for a class of jump–diffusion processes that are commonly used for modeling stock prices. For such financial models, related option pricing problems are often difficult, especially when the option under study is out-of-the-money and there are multiple underlying assets. Even though analytical pricing formulas do exist in a few very simple cases, often analysts must resort to numerical methods or Monte Carlo simulation. We demonstrate that efficient and easy-to-implement importance sampling schemes can be constructed via the method of cross-entropy combined with the expectation–maximization algorithm, when the alternative sampling distributions are chosen from the family of exponentially tilted distributions or their mixtures. Theoretical justification is given by characterizing the limiting behavior of the cross-entropy algorithm under appropriate scaling. Numerical experiments on vanilla options, path-dependent options and rainbow options are also performed to illustrate the use of this technology.